Journal Article

DKFZ-2021-00859

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A consistent version of distance covariance for right-censored survival data and its application in hypothesis testing.

Edelmann, D. (First author)DKFZ* ; Welchowski, T. ; Benner, A. (Last author)DKFZ*

2022
Wiley-Blackwell Malden, Mass. [u.a.]

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Please use a persistent id in citations: doi:10.1111/biom.13470

Abstract: Distance covariance is a powerful new dependence measure that was recently introduced by Székely et al. (2007) and Székely and Rizzo (2009). In this work, the concept of distance covariance is extended to measuring dependence between a covariate vector and a right-censored survival endpoint by establishing an estimator based on an inverse-probability-of-censoring weighted U-statistic. The consistency of the novel estimator is derived. In a large simulation study, it is shown that induced distance covariance permutation tests show a good performance in detecting various complex associations. Applying the distance covariance permutation tests on a gene expression dataset from breast cancer patients outlines its potential for biostatistical practice. This article is protected by copyright. All rights reserved.

Keyword(s): distance correlation ; distance covariance ; hypothesis testing ; nonlinear ; survival analysis

Note: #EA:C060#LA:C060# / 2022 Sep;78(3):867-879

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Contributing Institute(s):

1. C060 Biostatistik (C060)

Research Program(s):

1. 313 - Krebsrisikofaktoren und Prävention (POF4-313) (POF4-313)

Appears in the scientific report 2021

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; DEAL Wiley ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record

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